1. Field of the Invention
This invention relates to a method of data preparation for storing in a distributed data storage system, more particularly, the present invention relates to the method and controller for splitting of data for storage and retrieval of data with controllable redundancy for fault tolerant distributed data storage.
2. Description of the Related Art
With the growth of the use of the Internet, the need for data storage systems with the capability to manage large amounts of information has grown dramatically. Such data storage or information management systems must provide reliable service to millions of computer users.
In conventional data storage networks, large files are broken into smaller pieces and transmitted using a store-and-forward mechanism.
Anyone deploying a data storage or information management system must deal with insufficient communication channel bandwidth and the inability of computer hardware components to handle the data storage load.
One conventional approach to solving the problems of insufficient bandwidth and the inability of computer hardware to store sufficient amounts of data has been to build a distributed network data storage system. In a typical distributed network data storage system, data is stored on a network of computers that consists of a mesh of data transmission links, switching nodes, and end nodes. The data pieces are transmitted on a series of links that connect the source of the information and the actual destination nodes for the stored information. The data pieces are then reassembled at the destination node. The nodes along the path between the source of the information and its destination are primarily responsible for making sure that each data piece received is transmitted on the correct outgoing link so that the data properly reaches its destination.
To properly meet user demands for information, a distributed network data storage system must provide high-availability of stored data to the computers needing the stored data. Specifically, a distributed network data storage system should be able to stay on-line with consistent availability of uncorrupted data, even if some hardware portion of the distributed network data storage system has crashed or becomes inaccessible because of an inability to transmit data. This is shown in FIG. 1, where chunks 7 and 8 have become inaccessible due to a hardware 102 failure and a data transmission line 104 break, respectively.
To address the requirement for high-availability of stored data, one or more variations of a data minoring technique have been used in prior art data storage systems. In the execution of a data minoring technique, crucial data is simply duplicated in its entirety at several locations in the distributed data storage system. Special care must be taken to keep the data consistent across all locations where it is stored. However, full minoring of all data is costly both in hardware and in physical transfer time, particularly for large systems. One solution has been to keep the stored data consistent across all nodes, especially when the stored data could be changed on-line at several nodes simultaneously. This problem of keeping stored data consistent across all nodes in a data storage network is far from trivial.
There is little doubt that providing high-availability features in a distributed data storage system requires maintaining at least some level of redundancy of stored information. Historically, the problems associated with redundant data storage were addressed by the use of Redundant Arrays of Independent Disks (RAID) technology. The main concept behind RAID data storage technology is to divide the input data into units and then write/read several units of data simultaneously to several hard disk data storage systems. Several of the most commonly used configurations, or levels, of RAID arrays are described below.
The RAID Level 0 configuration implements a striped disk array for storing data. In a RAID Level 0 configuration, the data is broken down into blocks and each block is written to a separate data storage disk. The input/output performance of each disk drive is greatly improved by spreading the input/output load across many channels and disk drives. Reconstruction of the data set is accomplished by obtaining data blocks from each separate data storage disk.
The best data storage performance is achieved when the data to be stored is striped across multiple disk drives with each single disk drive attached to a single controller. No parity calculation overhead is involved, and there are no fault tolerance capabilities in the RAID Level 0 configuration. There is no fault tolerance in the RAID Level 0 configuration because a single disk drive is connected to a single controller. Accordingly, the failure of just one disk drive will result in corruption of the stored data.
The RAID Level 1 configuration implements what is known as “disc minoring.” Disc mirroring is done to assure the reliability of stored data and a high degree of fault tolerance. A RAID Level 1 configuration also enhances data read performance, but the improved data read performance and fault tolerance come at the expense of available capacity in the disk drives used to store data. Specifically, the data to be stored is copied and then stored on multiple disk drives (or “mirrored”). The storage of data on multiple disk drives assures that, should one disk drive fail, the data is available from another disk drive on which the same data has been stored. The data read performance gain of a RAID Level 1 configuration can be realized if the redundant data is distributed evenly on all of the disk drives of a mirrored set within the subsystem. In a RAID Level 1 configuration, the number of data read requests and total wait state times both drop significantly. These drops are inversely proportional to the number of hard drives used in a RAID Level 1 configuration.
A RAID Level 5 configuration data storage algorithm represents a data storage methodology between a RAID Level 1 configuration and a RAID Level 0 configuration. The RAID Level 5 configuration is the last of the most common RAID data storage arrays in use, and is probably the most frequently implemented.
A RAID Level 5 configuration is really an adaptation of the RAID Level 0 configuration that sacrifices some data storage capacity for the same number of disk drives. However, the RAID Level 5 configuration gains a high level of data integrity or fault tolerance. The RAID Level 5 configuration takes advantage of RAID Level 0's data striping methods, except that data is striped with parity across all of the disk drives in the array. The stripes of parity information are calculated using the “Exclusive OR” function. By using the Exclusive OR function with a series of data stripes in the RAID Level 5 configuration, any lost data can easily be recovered. Should any one disk drive in the array fail, the missing information can be determined in a manner similar to solving for a single variable in an equation (for example, solving for x in the equation, 4+x=7). In an Exclusive OR operation, the equation would be similar to 1−x=1. Thanks to the use of the Exclusive OR operation, there is always only one possible solution (in this case, 0), which provides a complete error recovery algorithm in a minimum amount of storage space.
A RAID Level 5 configuration achieves very high data transfer performance by reading data from or writing data to all of the disk drives simultaneously in parallel while retaining the means to reconstruct data if a given disk drive fails, thus maintaining data integrity for the data storage system.
A RAID Level 5 configuration minimizes the data write bottlenecks by distributing parity stripes over a series of hard drives. In doing so, a RAID Level 5 configuration provides relief to the concentration of data write activity on a single disk drive, in turn enhancing overall system performance.
The disadvantages of RAID-like implementation for distributed data storage systems are clear. First, it is impossible to dynamically control redundancy (classic RAID algorithms work in the case of failure of only one disk drive; if two or more disk drives go off line simultaneously, there is no way to recover data). Second, RAID technology does not scale for more than about ten disks, mainly due to the input/output intensive fault-recovery procedures that make the RAID technology unsuitable for systems where the unavailability of one or more nodes is common.
A similar data recovery problem arises when solving the problem of reliability of information transmission via communication channels. In this case algorithms of the Hamming error correction code (ECC)/error detection code (ECD) are usually used. In general, there are two approaches to solving the problem of reliability of information transmission. Selecting a particular approach to solving this problem usually depends on requirements associated with the information transmission process. Both of the requirements associated with the information transmission process require transmitting redundant information to recover data in case of error. The first approach, called error-correction code (ECC), introduces redundancy into the stored information in the form of extra bits transmitted together with a data block so that it is possible to recover erroneous bits using received block and error-correction bits. The second approach, called error-detection code (ECD), differs from the first approach in that one can only determine whether or not the data block contains errors without knowing which bits are incorrect.
One major drawback of both the error correction code and error detection code algorithms is that they are intended for data streaming recovery. Accordingly, these two algorithms carry a significant overhead in performance and amount of redundancy data. Even in case of errorless data transfer, one has to process a significantly larger amount of data than is necessary. Also, these two algorithms rely on the low probability of a channel error. In other words, these two algorithms work correctly only if the total number of errors in the received block of data does not exceed some predetermined number.
Accordingly, there still remains a need in the art for a system which permits the storage of large amounts of data across a distributed arbitrarily-connected network of storage elements with high reliability and fault tolerance.